Any published work which has made use of 3DNA should cite at least one of the following papers:
The current list of journal articles citing 3DNA can be found in Google scholar.
In this section, I will occasionally highlight papers that employ 3DNA in novel or significant ways. If you have a story to share, please let me know.
With great interest, I read the article titled Improving NMR Structures of RNA by Bermejo et al. As is well-known, solution NMR structures of RNA normally exhibit more steric clashes and conformational ambiguities than their crystal X-ray counterparts. The paper introduces an improved force field, RNA-ff1, for structure elucidation with Xplor-NIH. By adopting realistic atom radii and a new statistical torsional potential, the RNA-ff1 force field significantly enhances covalent geometry and MolProbity validation scores (in steric contacts and backbone conformation) in the seven tested NMR datasets.
I am glad to see that DSSR is mentioned in the Section titled Analysis of Known Structural Motifs:
… The program DSSR (Lu et al., 2015) (part of the 3DNA software suite [Lu and Olson, 2003, 2008]) was used to evaluate the stacking configuration of successive base pairs (i.e., ‘‘steps’’) within the helical stems of the systems in the present calculations. The most interesting trends are observed for the base-pair step parameters slide (Figure 4K) and rise (Figure 4L), which respectively measure an in-plane dislocation and the vertical displacement of a step relative to a local mid-step reference frame (Lu and Olson, 2003; for analysis of all step parameters, see Figure S1). Relative to A-form parameters in high-resolution X-ray structures (Olson et al., 2001) (Figures 4K and 4L, dashed lines), the average slide of all but one of the original NMR models (PDB: 1O15) is small in absolute value (Figure 4K). … Moreover, four out of the seven original PDB models display an average rise considerably larger than the expected 3.32 Å (the van der Waals separation distance between bases, not to be confused with the helical rise, measured relative to the helical axis, expected to be 2.83 Å for A-form [Olson et al., 2001]).
As an example, the single stem of PDB: 2KOC’s representative structure, assumed to be an A-form helix (Nozinovic et al., 2010), displays a particularly large separation between base pairs C3–G12 and A4–U11 (rise: 4.33 Å) that is visually evident when compared with that of the RNA-ff1 representative model (rise: 3.33 Å ) (Figure 6A). Indeed, this base-pair step defies conformational classification by DSSR in the PDB: 2KOC structure, while it is assigned as A-form (along with the rest of the stem) in the RNA-ff1 structure.
Through the text, the term “stem” or “helical stem” is used consistently, in line with the nomenclatures adopted by DSSR. It is worth noting that DSSR also derives a complete set of backbone conformational parameters, including the assignment of sugar-phosphate backbone suites. The backbone parameters constitutes only a small portion of what DSSR has to offer, and they are written to the auxiliary file dssr-torsions.txt by default.
The other day, I came across an article titled Different duplex/quadruplex junctions determine the properties of anti-thrombin aptamers with mixed folding by Krauss et al. published in Nucleic Acids Research (NAR). This NAR article draw my attention via Google Scholar alert because of its citation to the 2008 3DNA Nature Protocols paper, as shown below (in the Structural analysis section):
3DNA-dssr (41) was used to calculate local and overall geometric parameters of the aptamer. Superpose program from CCP4 package (42) was used to calculate root mean square deviations. Features of the thrombin–RE31 interface were calculated using Cocomaps server (43), whereas contacts between the two molecules, as well as packing interactions between the aptamer and symmetry related thrombin molecules, were found by using 3DNA-snap (41) and Pisa (44) programs. All the results were veri ed by visual inspec- tion of the structure with WinCoot (39).
Moveover, Table 2 lists Stacking interactions as calculated by 3DNA-DSSR (41) among residues belonging to the duplex, the junction and the quadruplex of RE31, with a note on the definition of base-stacking interactions:
Base-stacking is quantified as the area of the overlapped polygon de ned by the two bases of the interacting nucleotides, where the base atoms are projected onto the mean base plane.
To the best of my knowledge, this is the first time SNAP is mentioned in a peer-reviewed journal article. This paper also made good use of DSSR for the analysis of a complicated DNA structure (like RNA), with three non-canonical base pairs at the duplex/quadruplex junction (Figure 3) and extensive stacking interactions (Figure 4).
Figure 3. The duplex/quadruplex junction in RE31 aptamer.
Figure 4. Ribbon representation of RE31 highlighting the continuous stacking of bases from the duplex to the quadruplex region.
As this paper and those by Paul Paukstelis illustrate, DNA can adopt far more complicated 3D structures enabled by non-canonical base pairing schemes than the simple Watson-Crick paired double helices. 3DNA (including DSSR and SNAP) is well suited for the analysis of such extraordinary structures. On a different perspective, following 3DNA citations has become an effective way for me to keep in pace with relevant literature.
Recently, I noticed via Google Scholar the first citation to the paper DSSR, an integrated software tool for dissecting the spatial structure of RNA, recently published in Nucleic Acids Research (NAR). The citation is from Srinivas Somarowthu, in a review article titled Progress and current challenges in modeling large RNAs in the Journal of Molecular Biology. The JMB review article is concise, and overall a nice reading.
Specifically, in the section “Model Evaluation and Refinement”, DSSR is listed along with RNAView and MC-Annotate for the characterization of the secondary from 3D atomic coordinates, as below:
After building a model, it is essential to evaluate the quality, find any errors and refine the accordingly. First, it is important to make sure that all the base-pairs and the overall secondary structure is maintained correctly in the model. Tools such as RNAview [82], MC-Annotate [83], and DSSR [84] can calculate the secondary structure from a given 3D structure and thereby allow identification of problematic base-pairs. Recently, Antczak et al [85], developed a web server, RNApdbee, which integrates RNAview, MC-Annotate and DSSR, and extracts not only secondary structures but also kissing-loops and pseudoknots from a target tertiary model. Problematic base pairs can be fixed or rebuilt using interactive tools such as S2S/ASSEMBLE [45].
I am glad to see the first citation to the 2015 DSSR paper per se shortly after its publication in NAR. Looking forward, I can only expect more DSSR citations in diverse fields related to RNA structures.
On October 29, 2015, I performed a survey of citations to the following three 3DNA papers, using the Web of Science. The total number of citations are: NAR03 (787) + NP08 (184) + NAR09 (78) = 1049, spanning a diverse set of 191 journals in biology, chemistry, and material sciences. On the same date, Google Scholar reported 1360 citations for the same three papers.
- [NAR03] Lu, Xiang‐Jun, and Wilma K. Olson. “3DNA: a software package for the analysis, rebuilding and visualization of three‐dimensional nucleic acid structures.” Nucleic acids research 31.17 (2003): 5108-5121.
- [NP08] Lu, Xiang-Jun, and Wilma K. Olson. “3DNA: a versatile, integrated software system for the analysis, rebuilding and visualization of three-dimensional nucleic-acid structures.” Nature protocols 3.7 (2008): 1213-1227.
- [NAR09] Zheng, Guohui, Xiang-Jun Lu, and Wilma K. Olson. “Web 3DNA—a web server for the analysis, reconstruction, and visualization of three-dimensional nucleic-acid structures.” Nucleic acids research 37.suppl 2 (2009): W240-W246.
Among the 1049 citations in 191 journals, 694 citations (66%) are from the following 24 journals (~13%). The remaining 355 citations are from 167 other journals, including Cell (5 times), Science (2), Nature (3) and six additional Nature Publishing Group sub-journals (17).
1 Nucleic Acids Res (167)
2 J Phys Chem B (64)
3 Biochemistry (45)
4 J Am Chem Soc (45)
5 J Mol Biol (41)
6 Phys Chem Chem Phys (28)
7 Biophys J (25)
8 J Biol Chem (23)
9 PLoS One (23)
10 Acta Crystallogr D Biol Crystallogr (22)
11 J Chem Theory Comput (22)
12 Proc Natl Acad Sci U S A (22)
13 Bioinformatics (18)
14 Biopolymers (18)
15 J Biomol Struct Dyn (18)
16 J Chem Phys (18)
17 J Phys Chem A (16)
18 Structure (13)
19 RNA (12)
20 Biochem Biophys Res Commun (11)
21 Chem Res Toxicol (11)
22 J Comput Chem (11)
23 J Mol Model (11)
24 Nat Struct Mol Biol (10)
It is worth noting that while the Web of Science citation report is comprehensive, it is certainly not complete. In particular, citations in the online methods section seem not to be covered. For example, two 3DNA citations (on the DSSR program) in “Materials and Methods” (the Supplementary Materials) of two Science articles by the Ramakrishnan lab are missing from the list. Specifically, the Science papers employed DSSR for the characterization of RNA secondary structural features in crystal structures of the large ribosomal subunit and the whole ribosome of human mitochondria.
For those why are interested in knowing the details, click the link for the full reports of 3DNA citations. In the file, the citations are sorted in two ways: by citation numbers per journal, and by journal names.
It was a nice surprise to notice the following 3DNA citation in a Nature article, titled Selective small-molecule inhibition of an RNA structural element (doi:10.1038/nature15542). Moreover, the work came from Merck Research Laboratories, reporting a novel selective chemical modulator (ribocil) to repress riboswitch-mediated ribB gene expression and inhibit bacterial cell growth.
Homology modelling. A homology model of the E. coli FMN aptamer was constructed using program mutate_bases53 of the 3DNA package using the F. nucleatum impX riboswitch aptamer X-ray structure as the template and the FMN aptamer alignment of E. coli, F. nucleatum, P. aeruginosa and A. baumannii (Extended Data Fig. 5). All nucleotide insertions in the E. coli sequence were removed in the model (Extended Data Fig. 5). There are 34 base changes among the 111 nucleotides modelled. Base pairing when present remains consistent. Energy minimization at A92 was performed to avoid VDW clashes using Macromodel (Schrodinger, LLC).
In retrospect, the mutate_bases program was created in response to repeated requests from 3DNA users, initially mostly for modeling DNA-protein complexes. The program was first coded as a Perl script, and later on rewritten in ANSI C for efficiency. Since v2.1, mutate_bases has become an essential component of 3DNA, on a par with find_pair, analyze, rebuild and fiber etc. As I noted in the post documenting the program
Overall, mutate_bases has been designed to solve the in silico base mutation problem in a practical sense: robust and efficient, getting its job done and then out of the way. The program can have many possible applications: in addition to perform base-pair mutations in DNA-protein complexes, it should also prove handy in RNA modeling and in providing initial structures for QM/MM/MD energy calculations, and in DNA/RNA modeling studies.
The Merck Nature paper is the first time ever that the 3DNA mutate_bases program has been put in the spotlight. Hopefully more such applications/citations will appear in the future as the community begin to appreciate the value of this little gem.
Today, I noticed the paper do_x3dna: A tool to analyze structural fluctuations of dsDNA or dsRNA from molecular dynamics simulations by Kumar and Grubmuller in Bioinformatics (advance access published April 2, 2015). The summary reads:
The do_x3dna package has been developed to analyze the structural fluctuations of DNA or RNA during molecular dynamics simulations. It extends the capability of the 3DNA package to GROMACS MD trajectories and includes new methods to calculate the global-helical axis of DNA and bending fluctuations during simulations. The package also includes a Python module dnaMD to perform and visualize statistical analyses of complex data obtained from the trajectories.
I am aware of the do_x3dna package through the 3DNA Forum, and wrote a post DNA/RNA molecular dynamics trajectory analysis with do_x3dna on September 3, 2014. With this formal publication, the do_x3dna package will be more widely used, and 3DNA is likely to gain more recognition in the increasing relevant MD field.
While browsing through the November 2013-41(21) issue of NAR, I am please to find the following three citations to 3DNA, all under the Section of ‘Structural Biology’.
Such citations illustrate the prominent status of 3DNA for DNA structural analysis. I firmly believe that DSSR will make 3DNA a top player for RNA structural analysis in the not-too-distant future.
Recently I came across the following two direct citations to the 3DNA homepage x3dna.org:
http://x3dna.org/ 3DNA: Suite of software programs for the analysis, rebuilding, and visualization of three-dimensional nucleic acid structures.
- The review article Molecular Modeling of Nucleic Acid Structure by Galindo-Murillo et al. in Current Protocols in Nucleic Acid Chemistry in the section of “Model Building and Analysis Tools and Nucleic Acid Nomenclature” under INTERNET RESOURCES:
http://x3dna.org/
The 3DNA program for calculating helicoidal parameters in a consistent manner using a local helical axis definition.
As time goes by, I have every reason to believe that the x3dna.org website will become more noticeable in the literature. If you notice other such citations, please leave a comment.
Thanks to Google scholar, I recently become aware of the article by Mohammed AlQuraishi & Harley McAdams (2012) Three enhancements to the inference of statistical protein-DNA potentials” in Proteins: Structure, Function, and Bioinformatics. Reading through the text, I like it quite a bit. The abstract summarize the work well:
The energetics of protein-DNA interactions are often modeled using so-called statistical potentials, that is, energy models derived from the atomic structures of protein-DNA complexes. Many statistical protein-DNA potentials based on differing theoretical assumptions have been investigated, but little attention has been paid to the types of data and the parameter estimation process used in deriving the statistical potentials. We describe three enhancements to statistical potential inference that significantly improve the accuracy of predicted protein-DNA interactions: (i) incorporation of binding energy data of protein-DNA complexes, in conjunction with their X-ray crystal structures, (ii) use of spatially-aware parameter fitting, and (iii) use of ensemble-based parameter fitting. We apply these enhancements to three widely-used statistical potentials and use the resulting enhanced potentials in a structure-based prediction of the DNA binding sites of proteins. These enhancements are directly applicable to all statistical potentials used in protein-DNA modeling, and we show that they can improve the accuracy of predicted DNA binding sites by up to 21%.
I’m glad to find that the 3DNA mutate_bases program was used in deriving the statistical potentials of protein-DNA interactions:
The relative binding affinity of a protein to two different DNA sequences can be evaluated by computing the binding energy of the protein to those two sequences. This is done by mutating the DNA sequence in silico while keeping the protein fixed. We used the 3DNA software package for mutating DNA23,24, which maintains the backbone atoms of the DNA molecule but replaces the basepair atoms in a way that is consistent with the backbone orientation of the DNA.
For each base position, in silicon structural mutants are generated using 3DNA23,24 to mutate the basepair to include all four possibilities.
This is exactly one of the use cases I have in mind while creating the program:
Overall, mutate_bases has been designed to solve the in silica base mutation problem in a practical sense: robust and efficient, getting its job done and then out of the way. The program can have many possible applications: in addition to perform base-pair mutations in DNA-protein complexes, it should also prove handy in RNA modeling and in providing initial structures for QM/MM/MD energy calculations, and in DNA/RNA modeling studies.
With the recent refinement to allow for 3-letter nucleotide name in the standard base-reference frame file, mutate_bases now makes it exceedingly easy to mutate cytosine to 5-methylcytosine.
As more people get to know this 3DNA functionality, I am confident that mutate_bases will be more widely used.
